Elvis D.

Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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I’m excited to share that today at Instacart, we launched AI Solutions, a new suite of enterprise-grade tools designed to help every retailer compete and grow in an AI-first world. These technologies bring agentic and generative AI directly into the hands of grocers – making it easier to personalize shopping, improve operations, and make faster, smarter decisions. While AI is transforming grocery, it’s also redefining how retailers can serve their customers. With over a decade of data, partnerships across 1,800 retailers, and deep expertise in language, vision, and decision-making AI, we’re uniquely positioned to help the industry harness AI to create meaningful, personal experiences at scale. This is just the beginning of how we’re building technology that empowers retailers and elevates the grocery experience for everyone. You can read more about today’s news here: https://lnkd.in/g4BxqZsG

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Just wrapped the 7th Perplexity AI Business Fellowship session featuring Ali Ghodsi, CEO of Databricks, in conversation with Aravind Srinivas. Ali's refreshing candor about AI's enterprise impact, dropping some uncomfortable truths about enterprise AI that every engineering leader needs to hear: - "The only reason the AI bubble hasn't burst is inference-time compute" - not actual breakthroughs - When Databricks plugged advanced reasoning models into enterprise applications? "Barely any boost." The problem isn't intelligence but context - is "C" California or Celsius? - Success formula: unique data + distribution channels. Nothing else matters. - AI revolutionizes coding, which represents just 20% of engineering time. The other 80%? Human meetings and alignment work which AI can't touch. - Biggest blocker? Not capabilities but evaluation frameworks. "We have no way to measure reliability" in specialized enterprise contexts. This mirrors what we're seeing in healthcare AI - where reliable assessment frameworks are critical when patient outcomes are on the line. As I noted in my analysis of BVP's Healthcare AI Index (https://lnkd.in/gy3P7zdU), success isn't about algorithms but workflow integration and evaluation rigor. For engineering teams, the path forward isn't chasing fancier models but solving the semantic understanding and evaluation challenges that remain stubbornly human. #EnterpriseAI #EngineeringLeadership #HealthcareAI

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Over a year ago, we announced a significant milestone in reproductive medicine: the completion of the first-ever U.S. randomized controlled trial evaluating AI in embryo selection. This achievement represents years of dedicated work behind the scenes. Progress in IVF requires collaboration among clinics willing to challenge the status quo, researchers committed to rigorous science, and patients who participate in studies that advance the field. Over two years, 440 patients across seven leading fertility clinics took part in this landmark study, assessing how AI-assisted embryo selection can enhance clinical decision-making and improve the IVF process. Conducting a randomized controlled trial in reproductive medicine, particularly with emerging technologies like AI, is challenging but crucial. To responsibly integrate new technologies into the IVF lab and clinic, we must adhere to the highest standards of evidence. This work is a vital step toward a future where fertility care is more standardized, data-driven, and personalized for every patient. We extend our gratitude to the clinics, physicians, embryologists, and patients who made this study possible. We are proud to contribute to the scientific foundation that will shape the next generation of IVF care. https://lnkd.in/eapx-mrV

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A pattern I see across supply-chain businesses: Most problems are not caused by lack of effort. They’re caused by gaps in visibility, coordination, and decision timing. I wrote a short piece on the operating reality facing modern supply-chain organizations and how high-performing teams are responding differently. If you’re in packaging, manufacturing, or distribution, this will likely resonate. 👉 https://lnkd.in/guedw2Ty

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Reposting Musheer Ahmed’s update on Codoxo’s Series C—congratulations to the entire team and our investors. This milestone enables the next phase of our AI journey, and I wanted to share how we’re building toward our 2026 goals. 🚀 What’s next at Codoxo: the shift to #ReliableAI 🚀 With our Series C closed and strong momentum across national health plans, Codoxo is preparing for the next evolution of our AI platform. At the core is #ReliableAI: compliant by design, explainable in its decisions, fair in outcomes, and fully auditable—built for the realities of regulated healthcare. This transition will enable us to: • Operationalize Point Zero by applying intelligence upstream—before claims are created—reducing friction for providers and preventing issues before they occur • Act faster and more autonomously across the payment lifecycle with coordinated AI agents • Combine deterministic ML signals with GenAI reasoning to improve precision, scalability, and trust • Support our 2026 company goals with AI that is proactive, adaptive, and responsible by design Excited for what’s ahead as we scale this next chapter! #SeriesC #AgenticAI #ArtificialIntelligence #GenerativeAI #MachineLearning #HealthTech #HealthcareAI #PaymentIntegrity #EnterpriseAI #ResponsibleAI #ExplainableAI #AITransformation

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Databricks is making a major AI play with its $1 billion acquisition of database startup Neon. The deal highlights the exploding demand for AI agents—autonomous bots poised to become as ubiquitous as Stanley tumblers. “Every customer is eager to leverage agents," Databricks CEO Ali Ghodsi told the WSJ. But to function effectively, these AI tools require seamless database integration—Neon’s specialty. The startup’s cloud-based platform will connect with Databricks’ stored enterprise data, enabling smarter, more responsive AI agents. This strategic buy positions Databricks at the forefront of the AI-driven data analytics wave. #Databricks #Neon #AI #ArtificialIntelligence #TechNews #BigData #CloudComputing #StartupAcquisition #DataAnalytics #MachineLearning #AIAgents #TechTrends

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Excited to share Amazon’s recent feature on a water replenishment project in Mississippi that we at Arable are proud to support (read here: https://lnkd.in/dWU4BRkt). This initiative will reduce agricultural water withdrawals by 150 million gallons annually—that’s enough water to sustain over 1,600 households for a full year. By leveraging AI-powered Arable Mark 3 sensors, we’re helping farmers monitor soil moisture, weather, and crop needs in real time—giving them precise, data-driven irrigation guidance right on their phones. This smart-irrigation solution not only conserves vital water resources, but also strengthens the resilience and productivity of agricultural communities. Proud to see our work highlighted, and even prouder of the role we play in building more resilient agricultural systems.

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🚀 Choosing the Right AI & Data Platform Isn’t Just Technical—It’s Strategic At Lintu Solutions, we cut through the noise to help our clients make smarter, faster decisions about their data and AI stack. 🔍 Whether you’re navigating Palantir Foundry, Databricks, Snowflake, or AWS/GCP, your data platform should align with: ✅ Mission-critical goals ✅ Security requirements ✅ Team capabilities ✅ Long-term scalability We just published a side-by-side comparison of the top platforms—not just based on features, but on real-world enterprise fit. 📊 Check out our latest infographic below 👇 It’s built for decision-makers who need clarity, not complexity. 💬 Curious how your organization can harness these platforms to drive ROI in 90 days or less? Let’s talk. We specialize in secure, scalable AI adoption for business and government. #AIAdoption #DigitalTransformation #Palantir #Databricks #Snowflake #VertexAI #C3AI #LintuSolutions #GovTech #AIAssessment #DataStrategy #FederalContracting #AIDataPlatformComparison

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Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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Today we announced new capabilities for our Shop Direct and Buy for Me experiences on Amazon, enabling now feeds to facilitate external merchants to list reliably their product listings and reach millions of Amazon customers. Excited on this feature, that is a win/win experience for customers, merchants and brands. Excited to be part of such important project.

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This really feels like a turning point for retail and AI. Insights from Sundar Pichai at #NRF2026 underscore how AI is evolving beyond experimentation into an enterprise-wide operational engine – from discovery and personalized decisioning to seamless delivery and fulfillment. What stood out for me is how clearly the conversation has shifted from “𝐰𝐡𝐚𝐭 𝐜𝐚𝐧 𝐀𝐈 𝐝𝐨?” to “𝐡𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐫𝐮𝐧 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬?” The idea of a 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐂𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 is powerful—not because it’s flashy, but because open, interoperable standards are what make AI truly usable at scale across discovery, transactions, and fulfillment. For retailers, the real opportunity now is rethinking end-to-end experiences with AI embedded into the operating model, not bolted on as a feature. Leadership teams now have to answer, “𝘞𝘩𝘦𝘳𝘦 𝘥𝘰 𝘸𝘦 𝘳𝘦𝘥𝘦𝘴𝘪𝘨𝘯 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘤𝘩𝘢𝘪𝘯 𝘴𝘰 𝘈𝘐 𝘥𝘦𝘭𝘪𝘷𝘦𝘳𝘴 𝘴𝘱𝘦𝘦𝘥, 𝘴𝘪𝘮𝘱𝘭𝘪𝘤𝘪𝘵𝘺, 𝘢𝘯𝘥 𝘮𝘦𝘢𝘴𝘶𝘳𝘢𝘣𝘭𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘰𝘶𝘵𝘤𝘰𝘮𝘦𝘴?” The real advantage won’t come from who adopts AI first—but from who embeds it most thoughtfully into how the business actually runs. The winners won’t be the ones with the most demos, but the ones who translate this into simpler journeys, faster decisions, and measurable outcomes. Excited to see the industry move in this direction. I'm curious to know how other leaders are thinking about this shift - please share your thoughts in the comments box. #AILeadership #RetailTransformation #EnterpriseAI #TechStrategy #FutureOfRetail #CTOPerspective

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Supply chain may be one of the best vertical AI markets...and the hardest to build in. 🚛 It's a massive space, highly fragmented, and super manual. BUT it's also where legacy software, consultants, and brutal enterprise GTMs can crush a startup tackling the space. The opportunity is in owning workflows and replacing the human coordination. In this week's weekly musing, I wrote about: - how I think about the sub-areas of the monolithic "supply chain" - why exception-driven workflows are perfect for AI - why mid-market might be the right wedge - GTM mistakes to avoid Full read here: https://lnkd.in/gHHez2Sk

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I wrote about how product managers should deliberately decide which features should be built to an A grade and which ones should be built to a B or even only a C grade. Making the resource tradeoffs to optimize business outcomes is a core job of product management, and building every capability to an A grade is a poor use of scarce resources. https://lnkd.in/gTXBfNYE

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Listened to Lenny Rachitsky podcast with Marc Andreessen and it was a refreshing point of view (no hype) on AI and its impact. Marc blended “academic” rigor with operator-level instincts—wide lens, crisp conclusions Few takeaways: 1. A useful mental model for AGI: Framing AGI through cosmic vs. prosaic (with LLMs reaching human IQ level and getting better at task efficiency) made the conversation feel less hype-y and more actionable 2. Tasks are the atomic unit of work—not jobs: Which tasks get amplified, which get automated, and which new tasks become possible as the future of work gets defined 3. Models vs. application layers (and speed of wrappers) The gap between “frontier capability” and “productized value” is collapsing fast—especially when thin layers can ship in days, not quarters. E.g. Claude Cowork getting shipped in 10 days. An unusually good moment for Builders 4. Unexpected thoughtful parenting anecdotes were a delight: Hearing how he’s raising a 10-year-old in an AI-shaped world was as valuable as the tech conversation https://lnkd.in/g79tUkAd #AI #Productivity #FutureOfWork #Startups #ProductManagement

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When I was at League we were coordinating orchestration and other agents to drive better member usage. I had argued with AI, now is the time for something as pedantic and "useless" as health insurance apps could now not only drive outcomes immense, end user satisfaction, ROI, customer happiness but might also be on the track to win product awards. Nice to see the thinking being mapped out and on the way to production. I am sure for League this is just the beginning. And for those executives and nay-sayers on AI for their customers: get small wins, iterate and you will win. This is perfect example of iterating. Average time to have a successful AI really comes in edge case and evaluations takes about 6-12 months (now even faster) with RLHF, human-in-the-loop and other factors as technical prompt positioning, etc...... Also (special thanks on MAHESH YADAV) for introducing to me what is becoming a industry standard - more on this in another post, but he was the first person last February to share "evals of AI" based on HHH parameters. How helpful, honest, and harmless an agent can be and what methodologies can get an AI agent to be 95%+ effective.

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Shipping AI agents that *do* stuff, not just chat. These autonomous systems reason, plan, and execute complex goals–think real-time disaster response or optimizing crop yields. Goal, planner, memory, executor, action: that's the stack. See how it works: https://lnkd.in/g8UBjWUy

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